Abstract :
In classification tasks, class-modular strategy has been widely used. It has outperformed classical strategy for pattern classification task in many applications. However, in some modular architecture, such as one against all in support vector machines classifier, the training dataset for one class risks to heavily outnumber the other classes. In this challenging situation, the trained classifier will accurately classify the majority class; nevertheless, it marginalizes the minority class. As a result, True Negatives rate (TNr) will be very high while the True Positives rate (TPr) will be low. The main goal of this work is to improve TPr without much sacrifice in TNr. In this paper, we propose oversampling the minority class using polynomial fitting functions. Four new approaches were proposed: star topology, bus topology, polynomial curve topology and mesh topology. Star and mesh topologies approach had led to the best performances.
Keywords :
curve fitting; learning (artificial intelligence); mesh generation; pattern classification; polynomials; sampling methods; support vector machines; bus topology; class-modular strategy; imbalanced data set; mesh topology; oversampling approach; pattern classification task; polynomial curve topology; polynomial fitting function; star topology; support vector machine; true negative rate; true positive rate; Convergence; Data engineering; Pattern classification; Performance evaluation; Polynomials; Support vector machine classification; Support vector machines; Text analysis; Topology; Training data; class-modular strategy; imbalanced data sets; majority class; minority class; polynomial fitting functions; writer identification system;
